in machine learning, a decision tree is the result of a resource-intensive process by which a computing system processes a very large set of examples. These examples are used to construct a tree of questions that are used to ultimately classify input data at runtime. The more examples that are used during training of a decision tree, typically, the more accurate the runtime result. Traditionally the solution for processing millions to billions of examples is to use large clusters of networked central processing unit (CPU) based computing devices. However, this type of solution is expensive and is subject to unreliability. For example, additional components and connections are required in order to network clusters of CPU-based computing devices together, which create additional points of potential failure. Moreover, since the CPU-based computing device clusters are typically distributed over different areas, additional labor is required to maintain the clusters, which increases operating costs.